'''simple docstring'''





from typing import Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import get_image_size, pad, rescale, to_channel_dimension_format
from ...image_utils import ChannelDimension, ImageInput, make_list_of_images, to_numpy_array, valid_images
from ...utils import TensorType, logging


SCREAMING_SNAKE_CASE_:	int    =logging.get_logger(__name__)




class 			__A (					UpperCamelCase__      ):
    a__			:		Tuple			  =    ["""pixel_values"""]



    def __init__(self       :       int  ,     __a       :       bool = True  ,     __a       :       Union[int, float] = 1 / 255  ,     __a       :       bool = True  ,     __a       :       int = 8  ,     **__a       :       int  ,     ):
         super().__init__(**__a					)

         UpperCAmelCase_			       =  do_rescale
         UpperCAmelCase_			       =  rescale_factor
         UpperCAmelCase_			       =  do_pad
         UpperCAmelCase_			       =  pad_size



    def    _lowercase		(self       :       Optional[int]  ,     __a       :       np.ndarray  ,     __a       :       float  ,     __a       :       Optional[Union[str, ChannelDimension]] = None  ,     **__a       :       Optional[int]					):
         return rescale(__a  ,     scale=__a  ,     data_format=__a  ,     **__a					)



    def    _lowercase		(self       :       Optional[int]  ,     __a       :       np.ndarray  ,     __a       :       int  ,     __a       :       Optional[Union[str, ChannelDimension]] = None					):
         UpperCAmelCase_       ,					UpperCAmelCase_			       =  get_image_size(__a					)
         UpperCAmelCase_			       =  (old_height // size + 1) * size - old_height
         UpperCAmelCase_			       =  (old_width // size + 1) * size - old_width

         return pad(__a  ,     ((0, pad_height), (0, pad_width))  ,     mode="symmetric"  ,     data_format=__a					)






    def    _lowercase		(self       :       Tuple  ,     __a       :       ImageInput  ,     __a       :       Optional[bool] = None  ,     __a       :       Optional[float] = None  ,     __a       :       Optional[bool] = None  ,     __a       :       Optional[int] = None  ,     __a       :       Optional[Union[str, TensorType]] = None  ,     __a       :       Union[str, ChannelDimension] = ChannelDimension.FIRST  ,     **__a       :       List[str]  ,     ):
         UpperCAmelCase_			       =  do_rescale if do_rescale is not None else self.do_rescale
         UpperCAmelCase_			       =  rescale_factor if rescale_factor is not None else self.rescale_factor
         UpperCAmelCase_			       =  do_pad if do_pad is not None else self.do_pad
         UpperCAmelCase_			       =  pad_size if pad_size is not None else self.pad_size

         UpperCAmelCase_			       =  make_list_of_images(__a					)

         if not valid_images(__a					):
              raise ValueError(
                  "Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
                  "torch.Tensor, tf.Tensor or jax.ndarray."					)

         if do_rescale and rescale_factor is None:
              raise ValueError("Rescale factor must be specified if do_rescale is True."					)

         # All transformations expect numpy arrays.
         UpperCAmelCase_			       =  [to_numpy_array(__a					) for image in images]

         if do_rescale:
              UpperCAmelCase_			       =  [self.rescale(image=__a  ,     scale=__a					) for image in images]

         if do_pad:
              UpperCAmelCase_			       =  [self.pad(__a  ,     size=__a					) for image in images]

         UpperCAmelCase_			       =  [to_channel_dimension_format(__a  ,     __a					) for image in images]

         UpperCAmelCase_			       =  {"pixel_values": images}
         return BatchFeature(data=__a  ,     tensor_type=__a					)






"""simple docstring"""



from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


a       :						List[Any]       		=		logging.get_logger(__name__)

a       :						str       		=		{
    '''camembert-base''': '''https://huggingface.co/camembert-base/resolve/main/config.json''',
    '''umberto-commoncrawl-cased-v1''': (
        '''https://huggingface.co/Musixmatch/umberto-commoncrawl-cased-v1/resolve/main/config.json'''
    ),
    '''umberto-wikipedia-uncased-v1''': (
        '''https://huggingface.co/Musixmatch/umberto-wikipedia-uncased-v1/resolve/main/config.json'''
    ),
}




class 		a_    (			_UpperCAmelCase						):
    a   :					Any			     =					'camembert'







    def __init__(      self      :							Dict  ,       __UpperCamelCase      :							Union[str, Any]=3_05_22  ,       __UpperCamelCase      :							Tuple=7_68  ,       __UpperCamelCase      :							Optional[Any]=12  ,       __UpperCamelCase      :							int=12  ,       __UpperCamelCase      :							int=30_72  ,       __UpperCamelCase      :							int="gelu"  ,       __UpperCamelCase      :							Dict=0.1  ,       __UpperCamelCase      :							Dict=0.1  ,       __UpperCamelCase      :							List[Any]=5_12  ,       __UpperCamelCase      :							Optional[int]=2  ,       __UpperCamelCase      :							Dict=0.0_2  ,       __UpperCamelCase      :							Optional[int]=1e-12  ,       __UpperCamelCase      :							Optional[Any]=1  ,       __UpperCamelCase      :							int=0  ,       __UpperCamelCase      :							Optional[int]=2  ,       __UpperCamelCase      :							Optional[int]="absolute"  ,       __UpperCamelCase      :							int=True  ,       __UpperCamelCase      :							Dict=None  ,       **__UpperCamelCase      :							List[str]  ,       )		->Union[str, Any]:






        '''simple docstring'''






        super().__init__(pad_token_id=__UpperCamelCase  ,       bos_token_id=__UpperCamelCase  ,       eos_token_id=__UpperCamelCase  ,       **__UpperCamelCase  )

        _UpperCAmelCase        = vocab_size
        _UpperCAmelCase        = hidden_size
        _UpperCAmelCase        = num_hidden_layers
        _UpperCAmelCase        = num_attention_heads
        _UpperCAmelCase        = hidden_act
        _UpperCAmelCase        = intermediate_size
        _UpperCAmelCase        = hidden_dropout_prob
        _UpperCAmelCase        = attention_probs_dropout_prob
        _UpperCAmelCase        = max_position_embeddings
        _UpperCAmelCase        = type_vocab_size
        _UpperCAmelCase        = initializer_range
        _UpperCAmelCase        = layer_norm_eps
        _UpperCAmelCase        = position_embedding_type
        _UpperCAmelCase        = use_cache
        _UpperCAmelCase        = classifier_dropout







class 		a_    (			_UpperCAmelCase						):







    @property
    def   _snake_case       (      self      :							Optional[int]  )		->Mapping[str, Mapping[int, str]]:






        '''simple docstring'''






        if self.task == "multiple-choice":
            _UpperCAmelCase        = {0: """batch""", 1: """choice""", 2: """sequence"""}
        else:
            _UpperCAmelCase        = {0: """batch""", 1: """sequence"""}
        return OrderedDict(
            [
                ("""input_ids""", dynamic_axis),
                ("""attention_mask""", dynamic_axis),
            ]  )